Big data classification using fuzzy logical concepts for paddy yield prediction

Time association data has been critical to the exploration field of paddy yield forecast. At durations the path of recent many years, countless flossy legitimate time arrangement. For this reason, this paper canters round searching forward to statistics esteems on a huge variety of flossy precept ca...

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Autores:
Roca Cedeño, Jacinto Alex
García - López, Y.J
Choque Flores, Leopoldo
Morales-Ortega, Roberto
Neira-Molina, Harold
Combita-Niño, Harold
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8805
Acceso en línea:
https://hdl.handle.net/11323/8805
https://repositorio.cuc.edu.co/
Palabra clave:
classification
prediction
logical concepts
statistics
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openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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dc.title.spa.fl_str_mv Big data classification using fuzzy logical concepts for paddy yield prediction
title Big data classification using fuzzy logical concepts for paddy yield prediction
spellingShingle Big data classification using fuzzy logical concepts for paddy yield prediction
classification
prediction
logical concepts
statistics
title_short Big data classification using fuzzy logical concepts for paddy yield prediction
title_full Big data classification using fuzzy logical concepts for paddy yield prediction
title_fullStr Big data classification using fuzzy logical concepts for paddy yield prediction
title_full_unstemmed Big data classification using fuzzy logical concepts for paddy yield prediction
title_sort Big data classification using fuzzy logical concepts for paddy yield prediction
dc.creator.fl_str_mv Roca Cedeño, Jacinto Alex
García - López, Y.J
Choque Flores, Leopoldo
Morales-Ortega, Roberto
Neira-Molina, Harold
Combita-Niño, Harold
dc.contributor.author.spa.fl_str_mv Roca Cedeño, Jacinto Alex
García - López, Y.J
Choque Flores, Leopoldo
Morales-Ortega, Roberto
Neira-Molina, Harold
Combita-Niño, Harold
dc.subject.spa.fl_str_mv classification
prediction
logical concepts
statistics
topic classification
prediction
logical concepts
statistics
description Time association data has been critical to the exploration field of paddy yield forecast. At durations the path of recent many years, countless flossy legitimate time arrangement. For this reason, this paper canters round searching forward to statistics esteems on a huge variety of flossy precept calculations. To clarify the approach in the course of gauging, the verifiable statistics of paddy yield. The method for acknowledgment used at some point of this exam can also be an extreme information grouping. The technique joins the coaching capacities of fake neural device with the human like data portrayal and clarification capacities of flossy precept frameworks and furthermore a trendy primarily based in maximum instances hold close framework. It's miles for the most half of used in Brobdingnagian expertise getting equipped applications. As we have a tendency to in all opportunity am aware, affiliation method of massive information teams the information into thousands of categories addicted to high-quality trends for additional getting equipped. We've got engineered up some other calculation to have an effect on the grouping by using flossy recommendations on this present fact informational index. Forecast of harvest yield is significant because of this on precisely meet marketplace conditions and legitimate company of rural sports coordinated towards enhance in yield. A number of obstacles, as an example, weather, bothers, biophysical and physio morphological highlights advantage their idea whereas determining the yield. It's in reality proper right here that the flossy precept becomes partner in Nursing important issue. This paper explains a shot to create flossy valid frameworks for paddy crop yield expectation
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-10-26T12:48:05Z
dc.date.available.none.fl_str_mv 2021-10-26T12:48:05Z
dc.date.issued.none.fl_str_mv 2021
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dc.relation.references.spa.fl_str_mv Ahuja, S., Kumar, V., & Kumar, A. (2010). Fuzzy time series forecasting of wheat production. (IJCSE) International Journal on Computer Science and Engineering, 2(3), 635-640.
Chen, S.-M. (2002). FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES. Cybernetics and Systems, 33(1), 1-16. doi: 10.1080/019697202753306479
Garg, B., Beg, M. M. S., & Ansari, A. Q. (2011, 19-21 Oct. 2011). Employing genetic algorithm to optimize OWA-fuzzy forecasting model.Paper presented at the 2011 Third World Congresson Nature and Biologically Inspired Computing doi:10.1109/NaBIC.2011.6089609.
Hudec, M., & Vujošević, M. (2012). Integration of data selection and classification by fuzzy logic. Expert Systems with Applications, 39(10), 8817-8823. doi: https://doi.org/10.1016/j.eswa.2012.02.009
Krömer, P., Platoš, J., Snášel, V., & Abraham, A. (2011, 9-12 Oct. 2011). Fuzzy classification by evolutionary algorithms.Paper presented at the 2011 IEEE International Conference on Systems, Man, and Cybernetics doi:10.1109/ICSMC.2011.6083684.
Kumar, P. (2011). Crop yield forecasting by adaptive neuro fuzzy inference system.1(3), 8.
Kumar, S., & Kumar, N. (2012). A novel method for rice production forecasting using fuzzy time series. International Journal of Computer Science Issues (IJCSI), 9(6), 455.
Kumar, S., & Kumar, N. (2015). Two factor fuzzy time series model for rice forecasting. Int. J. Comput. Math. Sci, 4(1), 56-61.
Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443-1452. doi: https://doi.org/10.1016/j.agrformet.2010.07.008
Mehta, R. G., Rana, D. P., & Zaveri, M. A. (2009, 31 March-2 April 2009). A Novel Fuzzy Based Classification for Data Mining Using Fuzzy Discretization.Paper presented at the 2009 WRI World Congress on Computer Science and Information Engineering doi:10.1109/CSIE.2009.294.
Ortiz, M. J., Formaggio, A. R., & Epiphanio, J. C. N. (1997). Classification of croplands through integration of remote sensing, GIS, and historical database. International Journal of Remote Sensing, 18(1), 95-105. doi: 10.1080/014311697219295
Pandey, A., Sinha, A., & Srivastava, V. (2008). A Comparative Study of Neural-Network & Fuzzy Time Series Forecasting Techniques -Case Study: Wheat Production Forecasting. IJCSNS International Journal of Computer Science and Network Security, 8(9), 382–387.
Pendharkar, P. (2012). Fuzzy classification using the data envelopment analysis. Knowledge-Based Systems, 31, 183-192. doi: https://doi.org/10.1016/j.knosys.2012.03.007
Song, Q. (2003). A NOTE ON FUZZY TIME SERIES MODEL SELECTION WITH SAMPLE AUTOCORRELATION FUNCTIONS. Cybernetics and Systems, 34(2), 93-107. doi: 10.1080/01969720302867
Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series —part II. Fuzzy Sets and Systems, 62(1), 1-8. doi: https://doi.org/10.1016/0165-0114(94)90067-1
Vikas, L., & Dhaka, V. (2014). Wheat yield prediction using artificial neural network and crop prediction techniques (a survey). International Journal for Research in Applied Science and Engineering Technology, 2(9), 330-341.
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spelling Roca Cedeño, Jacinto AlexGarcía - López, Y.JChoque Flores, LeopoldoMorales-Ortega, RobertoNeira-Molina, HaroldCombita-Niño, Harold2021-10-26T12:48:05Z2021-10-26T12:48:05Z20212146-0353https://hdl.handle.net/11323/8805Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Time association data has been critical to the exploration field of paddy yield forecast. At durations the path of recent many years, countless flossy legitimate time arrangement. For this reason, this paper canters round searching forward to statistics esteems on a huge variety of flossy precept calculations. To clarify the approach in the course of gauging, the verifiable statistics of paddy yield. The method for acknowledgment used at some point of this exam can also be an extreme information grouping. The technique joins the coaching capacities of fake neural device with the human like data portrayal and clarification capacities of flossy precept frameworks and furthermore a trendy primarily based in maximum instances hold close framework. It's miles for the most half of used in Brobdingnagian expertise getting equipped applications. As we have a tendency to in all opportunity am aware, affiliation method of massive information teams the information into thousands of categories addicted to high-quality trends for additional getting equipped. We've got engineered up some other calculation to have an effect on the grouping by using flossy recommendations on this present fact informational index. Forecast of harvest yield is significant because of this on precisely meet marketplace conditions and legitimate company of rural sports coordinated towards enhance in yield. A number of obstacles, as an example, weather, bothers, biophysical and physio morphological highlights advantage their idea whereas determining the yield. It's in reality proper right here that the flossy precept becomes partner in Nursing important issue. This paper explains a shot to create flossy valid frameworks for paddy crop yield expectationRoca Cedeño, Jacinto AlexGarcía - López, Y.JChoque Flores, LeopoldoMorales-Ortega, RobertoNeira-Molina, HaroldCombita-Niño, Haroldapplication/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Review of International Geographical Education Onlinehttps://rigeo.org/submit-a-menuscript/index.php/submission/article/view/1395classificationpredictionlogical conceptsstatisticsBig data classification using fuzzy logical concepts for paddy yield predictionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAhuja, S., Kumar, V., & Kumar, A. (2010). Fuzzy time series forecasting of wheat production. (IJCSE) International Journal on Computer Science and Engineering, 2(3), 635-640.Chen, S.-M. (2002). FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES. Cybernetics and Systems, 33(1), 1-16. doi: 10.1080/019697202753306479Garg, B., Beg, M. M. S., & Ansari, A. Q. (2011, 19-21 Oct. 2011). Employing genetic algorithm to optimize OWA-fuzzy forecasting model.Paper presented at the 2011 Third World Congresson Nature and Biologically Inspired Computing doi:10.1109/NaBIC.2011.6089609.Hudec, M., & Vujošević, M. (2012). Integration of data selection and classification by fuzzy logic. Expert Systems with Applications, 39(10), 8817-8823. doi: https://doi.org/10.1016/j.eswa.2012.02.009Krömer, P., Platoš, J., Snášel, V., & Abraham, A. (2011, 9-12 Oct. 2011). Fuzzy classification by evolutionary algorithms.Paper presented at the 2011 IEEE International Conference on Systems, Man, and Cybernetics doi:10.1109/ICSMC.2011.6083684.Kumar, P. (2011). Crop yield forecasting by adaptive neuro fuzzy inference system.1(3), 8.Kumar, S., & Kumar, N. (2012). A novel method for rice production forecasting using fuzzy time series. International Journal of Computer Science Issues (IJCSI), 9(6), 455.Kumar, S., & Kumar, N. (2015). Two factor fuzzy time series model for rice forecasting. Int. J. Comput. Math. Sci, 4(1), 56-61.Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443-1452. doi: https://doi.org/10.1016/j.agrformet.2010.07.008Mehta, R. G., Rana, D. P., & Zaveri, M. A. (2009, 31 March-2 April 2009). A Novel Fuzzy Based Classification for Data Mining Using Fuzzy Discretization.Paper presented at the 2009 WRI World Congress on Computer Science and Information Engineering doi:10.1109/CSIE.2009.294.Ortiz, M. J., Formaggio, A. R., & Epiphanio, J. C. N. (1997). Classification of croplands through integration of remote sensing, GIS, and historical database. International Journal of Remote Sensing, 18(1), 95-105. doi: 10.1080/014311697219295Pandey, A., Sinha, A., & Srivastava, V. (2008). A Comparative Study of Neural-Network & Fuzzy Time Series Forecasting Techniques -Case Study: Wheat Production Forecasting. IJCSNS International Journal of Computer Science and Network Security, 8(9), 382–387.Pendharkar, P. (2012). Fuzzy classification using the data envelopment analysis. Knowledge-Based Systems, 31, 183-192. doi: https://doi.org/10.1016/j.knosys.2012.03.007Song, Q. (2003). A NOTE ON FUZZY TIME SERIES MODEL SELECTION WITH SAMPLE AUTOCORRELATION FUNCTIONS. Cybernetics and Systems, 34(2), 93-107. doi: 10.1080/01969720302867Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series —part II. Fuzzy Sets and Systems, 62(1), 1-8. doi: https://doi.org/10.1016/0165-0114(94)90067-1Vikas, L., & Dhaka, V. (2014). Wheat yield prediction using artificial neural network and crop prediction techniques (a survey). 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